Introducing a Breakthrough Technology: The Quartic Process Optimizer
Achieve ROI in 90 days or less, no process models, no big data required.
"With Quartic we have reduced batch to batch variability by 79%"
- Global Food and Beverage Company
Benefits
- Optimize any batch unit operation and build a multi-unit optimizer
- Optimization results can be achieved in as little as 10 batch runs
- Simple deployment results in accelerated ROI
Differentiators
- Large amounts of historical data sets are not required
- No first-principle process models or digital twins are needed
- You can optimize single or multiple KPIs under any number of constraints
Additional Content
Achieve Successful Outcomes with AI for Process Optimization
In this webinar Russ Rhinehart, hall of fame Optimization expert, and Xiaozhou Wang, Chief Data Scientist of Quartic.ai describe the shortcomings and challenges of legacy process optimization systems and how modern, AI based approaches can overcome them.
Optimizing Continuous Manufacturing Processes
In this blog post, we will walk through one example of a continuous manufacturing process and demonstrate how advanced process control and machine learning optimization can play a key role in the future of continuous manufacturing.
To achieve Agility in manufacturing – embrace Variability – Part 1
With supply chain disruptions and the increasing pace of new product introductions, variability has almost become a constant for process manufacturers. In this blog series, Rajiv Anand, founder, and CEO of Quartic.ai discusses how to embrace variability to build an agile manufacturing enterprise.
Optimization with Offline Reinforcement Learning
In this article, the Quartic.ai research team discusses the results of two offline reinforcement learning approaches - by conservative Q learning and MOPO - inspired by Decision Transformer
